Detection in Mobile Service Maintenance

ABSTRACT

Disclosed is a method and system for detecting inconsistencies between a radio communications network and a network database. In one form, measurements from the network are provided by mobile radio terminals. The measurements are then compared with corresponding data on the network database to determine whether there is an inconsistency. The methods described may be used in the management and maintenance of the network.

TECHNICAL FIELD

The present invention relates to mobile communication networks and totheir management.

PRIORITY DOCUMENTS

The present application claims priority from:

Australian Provisional Patent Application No. 2005905863 entitled“Mobile Service Maintenance” filed on 24 Oct. 2005.; andAustralian Provisional Patent Application No. 2005906105 entitled“Profile Based Communications Service” filed on 4 Nov. 2005.

The entire content of each of these applications is hereby incorporatedby reference.

INCORPORATION BY REFERENCE

The following co-pending patent applications are referred to in thefollowing description:

-   -   PCT/AU2005/001358 entitled “Radio Mobile Unit Location System”;    -   PCT/AU2006/000347 entitled “Enhanced Mobile Location Method and        System”;    -   PCT/AU2006/000348 entitled “Enhanced Mobile Location”    -   PCT/AU2006/000478 entitled “Enhanced Terrestrial Mobile        Location”    -   PCT/AU2006/000479 entitled “Mobile Location”    -   PCT/AU2006/001479 entitled “Profile Based Communications        Service”    -   Co-pending International Patent Application entitled “Mobile        Service Maintenance Management” filed concurrently herewith and        claiming priority from Australian Provisional Patent Application        No. 2005905863    -   Section 2.7 of Mobile Radio Communications 2^(nd) Ed. Editors        Steele and Hanzo. ISBN 047197806X,J. Wiley & Sons Ltd, 1999    -   Section 5.1.4 of “Radio Frequency (RF) system scenarios” 3GPP        TR25.942    -   Section 3.2 of “Evaluation of Positioning Measurement Systems”,        T1P1.5/98-110).

The entire content of each of these documents is hereby incorporated byreference.

BACKGROUND

Radio communication networks often use information representing certaincharacteristics or parameters of different parts of the network. Oneexample of an application that uses this information is a mobile radiolocation system. Some mobile radio location systems operate by usingradio measurements to estimate the location of mobile terminals relativeto the known locations of the radio network access points. For thespecial case of cellular mobile phone location systems these accesspoints are the cells.

A location system which estimates the location of a mobile terminalrelative to one or more radio network access points requires knowledgeof the relevant characteristics of those access points. For example, ina coarse cell identifier based mobile cellular location system, therelevant characteristics typically include the unique identifier for thecell and the geographical coordinates at which the cell is situated.

More accurate systems such as those which also incorporate radio signalmeasurements in the calculation process require additional configurationinformation. This typically includes transmitted power, antenna gain andantenna orientation.

The performance of such systems is strongly dependent on the integrityof the database containing this network configuration information. Thisdependence increases in systems promising greater levels of spatialresolution or accuracy. In an ideal scenario, the configuration of thecellular network will match the network database. In such a scenario alocation system would only need to cope with changes to the networkconfiguration which would be notified via an update to the networkdatabase. Experience has shown however that typically the configurationinformation is poorly maintained, distributed across multiple databasesand exhibits many errors.

Reasons for discrepancies between the supplied database and actualconfiguration may lie with the network database or with the networkconfiguration or both. The database may be at fault due to errors suchas typographical errors, especially the transposition of numbers, duringdata entry; problems with the process used to collect and collate thenetwork data; and failure to propagate network configuration changes tothe database. Conversely the network configuration may not be asintended due to errors such as typographical errors when enteringconfiguration details and failure to configure one or more plannednetwork changes.

A further problem for operators is that the network configuration is notstatic. Opportunities for inconsistencies to arise between the networkdatabase and deployed configuration occur throughout the life of thenetwork. The network configuration changes when sites are added toincrease capacity and/or coverage. Changes also occur when cells aredecommissioned. Mobile cells (referred to as Cells-On-Wheels) can betemporarily setup to support the temporary capacity increases requiredto support events such as significant sporting events and outdoor musicconcerts. These temporary additions and deletions to the network canlast for hours and in some cases days. The configuration may changetemporarily when there is a cell not operating due to scheduledmaintenance, equipment failure, or power failure. The network alsochanges when technicians retune the network to improve performance or toadapt to changes due to reasons discussed above.

Network database errors lead to corresponding errors in the operation ofthe location-based system and associated services, in some cases leadingto unacceptable service quality for subscribers. Network operators haveno means of validating that the network is configured as planned otherthan to perform drive tests around the network with radio monitoringequipment. The cost of updating the database so that it is continuallyup-to-date represents a significant operational burden for the serviceprovider.

It is an object of the present invention to detect errors and/orinconsistencies between a configured network and corresponding networkdatabases.

SUMMARY

In one aspect of the present invention, there is provided a method fordetecting an inconsistency between a radio communications network and anetwork database, the method comprising:

-   -   receiving from a mobile radio terminal in the radio        communications network, at least one measurement of at least one        parameter from the mobile radio communications network;    -   comparing the at least one measurement with corresponding data        in the network database; and    -   determining that the at least one measurement is inconsistent if        the at least one measurement is different to the corresponding        data in the network database.

In one form, the method further comprises the step of calculating ametric associated with the at least one measurement using data from thenetwork database, and comparing the calculated metric with a threshold.

In another form, the method further comprises determining that the atleast one measurement is different to the corresponding data in thenetwork database if the calculated metric exceeds the threshold.

In another form, the method further comprises making a hypothesis that aparameter of the mobile radio communications network is not present inthe mobile radio communications network even though data in the networkdatabase indicates that the parameter is present.

In a further form, the method further comprises, if the at least onemeasurement does not contradict the hypothesis, considering data thatsupports the hypothesis.

In a further form, the method further comprises considering data thatsupports the hypothesis.

In one form, the step of considering data that supports the hypothesiscomprises determining whether the mobile radio terminal is in a givenzone.

In another form, if the mobile radio terminal is determined to be in thegiven zone, the method further comprises comparing the at least onemeasurement with data in the network database corresponding to one ormore expected measurements that would be expected to be obtained by themobile radio terminal in the given zone.

In another form, the method further comprises determining that there isan inconsistency between the radio communications network and thenetwork database if the step of comparing the at least one measurementwith data in the network database corresponding to the one or moreexpected measurements indicates a difference.

In a further form, the method further comprises accumulating a pluralityof measurements over time and determining that there is an inconsistencybetween the radio communications network and the network database if thedifference between the accumulated measurements and the one or moreexpected measurements exceeds a predetermined threshold.

In one form, the hypothesis is that the radio communications networkcontains a non-operational cell.

In another form, the at least one measurement is received from themobile radio terminal using spare capacity in an already establishedcommunications session.

In another form, a plurality of measurements are received from aplurality of mobile radio terminals within the radio communicationsnetwork. According to another aspect of the present invention, there isprovided a network processor in a radio communications network having atleast one radio parameter, at least one mobile radio terminal, and anetwork database, the network database storing data corresponding to theat least one radio parameter, the network processor comprising:

-   -   a receiver for receiving from the mobile radio terminal in the        radio communications network, at least one measurement of the at        least one parameter;    -   a comparator for comparing the at least one measurement with the        corresponding data in the network database; and    -   a means for determining that the at least one measurement is        inconsistent if the at least one measurement is different to the        corresponding data in the network database.

In another form of the present invention, there is provided a radiocommunications network comprising a network processor according to theprevious aspect of the present invention.

According to another aspect of the present invention, there is provideda method for detecting a non-operational cell in a radio communicationsnetwork, the method comprising:

-   -   receiving at least one measurement, including data relating to        at least one cell, from a mobile radio terminal in the radio        communications network;    -   determining whether the mobile radio terminal is in a given        zone; determining whether the at least one cell is reported;    -   updating evidence that the at least one cell is not operating;    -   determining whether the updated evidence exceeds a predetermined        threshold; and    -   determining that the at least one cell is not operational if the        updated evidence exceeds the predetermined threshold.

In one form, the method further comprises, for each cell reported in theat least one measurement, resetting evidence against the at least onecell not operating.

In one form, the step of resetting evidence against the at least onecell not operating comprises setting an accumulated unreported cell costto zero and setting a cell count to zero.

In another form, for each cell unreported in the at least onemeasurement, the step of updating evidence that the at least one cell isnot operating comprises computing an unreported cell cost for the atleast one cell and adding the computed unreported cell cost to theaccumulated unreported cell cost and incrementing the cell count.

In one form, the step of determining whether the updated evidenceexceeds the predetermined threshold comprises determining whether theaccumulated unreported cell cost is greater than the predeterminedthreshold.

In another form, the at least one cell is determined to be potentiallynon-operational if the accumulated unreported cell cost is greater thanthe predetermined threshold.

According to yet another aspect of the present invention, there isprovided a network processor for use in a radio communications networkhaving at least one cell in a zone and at least one mobile radioterminal in the radio communications network, the network processorcomprising:

-   -   a receiver for receiving at least one measurement, including        data relating to at least one cell, from a mobile radio terminal        in the radio communications network;    -   a means for determining whether the mobile radio terminal is in        a given zone; determining whether the at least one cell is        reported;    -   a means for updating evidence that the at least one cell is not        operating;    -   a means for determining whether the updated evidence exceeds a        predetermined threshold; and    -   a means for determining that the at least one cell is not        operational if the updated evidence exceeds the predetermined        threshold.

According to another aspect of the present invention, there is provideda radio communications network comprising a network processor accordingto the previous aspect.

DRAWINGS

Various aspects of the present invention will now be described withreference to the following Figures in which:

FIG. 1—shows a system architecture of an exemplary radio communicationsnetwork to which various aspects of the present invention may beapplied;

FIG. 2—shows a network arrangement in which a cell identity error arisesfrom a cell coordinate error;

FIG. 3—shows a flowchart of a method of directly detecting aninconsistency;

FIG. 4—shows a flowchart of a method of indirectly detecting aninconsistency by accumulated observations;

FIG. 5—shows a flowchart of a method of detecting the presence of anunknown and incorrectly identified Cell;

FIG. 6—shows a flowchart of a method of detecting non-operational cells;

FIG. 7—shows a flowchart of a method of detecting a non-operational cellbecoming operational;

FIG. 8—shows a flowchart of a method of detecting cells with incorrectcoordinates;

FIG. 9—shows a flowchart of a method of detecting cells with incorrectcoordinates using probability metric;

FIG. 10A—illustrates the detection of incorrect cell coordinates forcells A and B heard contemporaneously;

FIG. 10B—illustrates the detection of incorrect cell coordinates forcells A and C heard contemporaneously; and

FIG. 10C—illustrates the detection of Cells A and C of FIG. 10B inanother example.

DETAILED DESCRIPTION

Various aspects of the present invention will now be described in detailwith reference to one or more embodiments of the invention, examples ofwhich are illustrated in the accompanying drawings. The examples andembodiments are provided by way of explanation only and are not to betaken as limiting to the scope of the invention. Furthermore, featuresillustrated or described as part of one embodiment may be used with oneor more other embodiments to provide a further new combination.

Although many of the examples used to illustrate the embodiments of thepresent inventions are based on the GSM mobile phone system, theembodiments disclosed herein are readily applied to other mobile phonesystems such as UMTS, CDMA-2000, and CDMA IS-95. This is because theparameters being measured and the corresponding cell characteristicshave equivalents in each of the mobile phone technologies. For example,a GSM signal strength measurement can be used in the same way as aCDMA-2000 pilot power measurement. As another example, just as theabsence of a cell from a GSM Network Measurement Report may indicate anon operational cell, the absence of a particular UMTS Node B from a setof intra frequency measurements may also indicate a non-operationalcell.

It will be understood that the present invention will cover thesevariations and embodiments as well as variations and modifications thatwould be understood by the person of ordinary skill in the art.

Throughout this specification, the term “mobile” or “mobile phone” isused synonymously with terms such as “cell phones” or “mobile radioterminal”, and will be understood to encompass any kind of mobile radioterminal such as a cell phone, Personal Digital Assistant (PDA), lap topor other mobile computer, or pager. Similarly the terms cell is usedsynonymously with the term cell.

Throughout this specification the term “location system” is used in itsmost general sense referring to systems that output location estimateswith respect to an object or coordinate frame and to systems that outputthe location estimate as an indication of the proximity to an object oran area. This includes, but is not limited to, zone-based locationsystems such as that described in PCT/AU2006/000478 entitled “EnhancedTerrestrial Mobile Location”.

The term “about” as used herein may be applied top modify anyquantitative representation that could be permissively vary withoutresulting in a change in the basic function to which it is related.

In the following description, when processing is described as beingcarried out in a mobile terminal, it will be understood that theprocessing could be carried out in the handset, in the SubscriberIdentification Module (SIM) that is inserted in the handset, in anadditional processing or smart card inserted into the handset, or in acombination of two or more of these.

In this specification, use of the term network configuration refers tothe as deployed network and where relevant also includes the operationalstate of each component of the network.

It will also be understood that much of the processing that occurs inthe implementation of various aspects of the present invention can alsobe distributed between the handset, one or more network elements orprocessors within the radio communications network and/or one or moreelements outside the radio communications network. It will also beunderstood that the invention may be applied to any application in whicha location estimate for a mobile terminal is required.

Furthermore, the network database referred to in the various aspects ofthe present invention can be a central repository, a distributeddatabase and/or optionally with full or partial copies distributed toone or more mobile radio terminals.

While the following description uses location and zone based systems toexemplify the operation of the invention, it will be appreciated thatthe invention is not limited to such applications. The methods describedare equally useful for other systems in which a radio networkconfiguration database is maintained as will be understood by one ofordinary skill in the art. One such example is the primary operation ofthe mobile network in providing voice and data communications whereproblems with the network configuration degrade the quality of serviceand/or coverage.

System Architecture

FIG. 1 shows an exemplary mobile radio network arrangement in which thevarious processes may be applied. Shown there is a radio communicationsnetwork 10 containing a number of Base Stations 11 for communicatingwith one or more radio mobile terminals 20. Also associated with network10 is a network processor, such as Location Server 30 and networkdatabase 50. Network database 50 may store any kind of data, including amodel of the network 10. A system operator 40 may also be present formanaging various aspects of the network 10. Network processor 30 mayhave all the required apparatus for carrying out the various aspects ofthe present invention, including one or more receivers for receivingdata from various network elements such as mobile radio terminals,comparators for comparing data received from the network elements withdata in the network database, processors for performing variouscalculations and computations, and means for outputting the results ofthese various calculations and computations.

The following sections discuss the different types of network databaseerrors and the impact such errors have can on location estimation andzone-based location systems.

Configuration Vs Database Errors

Any discrepancy between the as configured mobile network and the networkdatabase that is supposed to reflect the as configured network does notnecessarily mean that it is the network database that needs to bechanged. The problem may be that an intended change to the configurationof the network was not carried out or was carried out incorrectly andthat it is the configuration that needs to be corrected to ensure theconfigured network and network database are in synchronisation.

Operational Cell Not in Database

There is a range of scenarios in which a cell can be operational but notin the network database. By ‘operational’, it is meant that the cell isavailable for use by a mobile terminal in the vicinity. While thescenarios are different, the effect of the omission on a location systemas a practical matter can cause similar operational issues.

A cell may be operating but not necessarily in the network database. Thecell site may have been recently commissioned and the database notupdated to reflect the addition of the new cells. The network databasemay have a cell ID error such that the broadcast cell ID does not matchthat in the database. The cell may be due for decommissioning and wasprematurely removed from the network database. The cell may be atemporary cell, commonly referred to as a Cell-On-Wheels (COW), toprovide coverage for a short term localized increased in capacityrequirements as would occur for large sporting events or festivals.

In a location system, the effect on location estimates of an operationalcell being omitted from the network database can degrade performance. Aswill be described in more detail further below, the cell ID of theserving cell is, in some circumstances, critical for determining thesource of the neighbour cell measurements. If the mobile terminal hasused as its serving cell a cell that is not in the database, then theneighbour cells may not be able to be identified and the locationtransaction will not be fulfilled. At the very least any measurementsfrom omitted cells cannot be used as the location of the cell cannot bedetermined. This results in a drop in the accuracy of the locationestimate and could potentially lead to a failed transaction if thereremain too few measurements to enable the location estimate to becomputed.

For zone-based location systems, the effects of not updating the networkdatabase with a new cell can adversely impact the system performance.For a Cell ID based zone system, any subscriber whose zone lies withinthe range of the new cell could find their service degraded as whenevertheir mobile terminal camps on the new cell, the mobile may be deemed tobe out of the zone even though they may physically be within the zone.

In PCT Patent Application No. PCT/AU2006/000478, the zone computationwill consider the measurement of a cell that it does not know as beingevidence that the mobile is not in the zone. The overall effect may beto shrink the zone and if the signal strength is sufficiently high themobile radio terminal may not register ever being in the zone.

Non-Operational Cell in Database

Although a cell is included in a network database it may not necessarilybe operating at a given point in time. The cell may be new and not yetmade operational. The cell may be temporarily non-operational due to afault or planned maintenance. The cell's cell identity may be correctlyentered and hence from the viewpoint of measurements made the cellidentity as listed in the database is never observed and is thus assumednon-operational. The cell may have been decommissioned but not yetremoved from the database.

In certain location systems, the presence of a non-operational cell orcells in the database may not degrade location performance. In a systemutilising unreported cells in estimating location such as that describedin PCT Patent Application No. PCT/2006/000347, the existence ofnon-operational cells in the database may degrade the accuracy of thesystem. By referring to a cell as unreported, we mean that it is notreported in a particular set of measurements from a given mobileterminal.

For a Cell ID based zone system, the presence of non-operational cellsin the network database may not degrade the performance. However, in azone-based system such as that described in the previously-mentioned PCTPatent Application No. PCT/AU2006/000478, there may be a degradation inperformance. To illustrate, the definition of a zone measured when adominant cell was operational is likely to feature that cell Should thatcell become non-operational it will no longer be reported by a mobileterminal in that zone. This in turn may lead a zone detection system toinfer that the mobile is not situated within the zone, unless thedatabase is updated to reflect the non-operational status of that cell.

Cell Identification Parameter Errors

Base stations typically have a unique cell identifier. In the case ofGSM the Local Area Code (LAC) and Cell Identifier (CID) uniquelyidentify a particular cell within a given network. Base stations alsohave other attributes that can identify the cell to within a subset ofthe cells in a network. Such attributes include the transmissionfrequency (ARFCN in GSM, UARFCN in 3G UMTS), the BSIC in GSM and PSC in3G UMTS. When a network is retuned it is common for the transmissionfrequency and BSIC/PSC to be changed and as such a retune represents anopportunity for discrepancies to arise between the as configured networkand the network database.

Errors in the Cell ID, in certain circumstances, will manifest as anapparent new cell in mobile measurements and a cell that appears to benon-operational in so far that the cell in the database is neverreported in any sets of measurements.

When measurements are made of a given cell, the unique cell identifieris usually only obtained for the serving cell. For the neighbour cellsthe measurements contain one or more of the non-unique identifiers. Forexample in GSM the BSIC and ARFCN are easily obtained; for 3G UMTS, thePSC and UARFCN are easily obtained. Identification of the cell isachieved by finding that cell with the observed attributes that is themost likely to be heard given the reported serving cell. Techniques fordoing this are well known in the art and include finding the cellmatching the criteria nearest to the serving cell, and the use ofpropagation models to determine the matching cell most likely to beheard given the area where the serving cell would be expected to bestrongest. Examples of suitable propagation models include the Hatamodel (see section 2.7 of Mobile Radio Communications 2nd Ed. EditorsSteele and Hanzo, ISBN 047197806X,J. Wiley & Sons Ltd, the 3GPP model(see section 5.1.4 of “Radio Frequency (RF) system scenarios” 3GPPTR25.942) and the T1P1.5 model (see section 3.2 of “Evaluation ofPositioning Measurement Systems”, T1P1.5/98-110).

Errors in the non-unique cell identifiers may create problems forlocation systems as the measurements may be associated with the wrongcell or perhaps not associated with a cell at all. Consequently thelocation estimate or zone detection may be in error.

FIG. 2 illustrates this as an example, in which a mobile radio terminal20 is camped on cell 6692. Mobile radio terminal 20 also hears cell 3451but since it is not camped, the cell is only identified via thenon-unique identifiers: ARFCN=76 and BSIC=55. In the Location Server 30,network measurements sent by the mobile radio terminal 20 are analyzed.One step is to uniquely identify those cells not uniquely identified inthe measurements sent by the mobile radio. This is done by searching fora match to the non-unique identifier that is closest to the known(serving) cell. In this example a cell coordinate error results in cell7587 being selected as the best match. The cell is illustrated as afaded tower to indicate that the cell is not actually the locationindicated in the network database. Had the cell not had incorrectcoordinates, the correct cell 3451 would have been associated with themeasurements.

Incorrect Cell Coordinates

Location estimates are particularly affected by incorrect cellcoordinates. In general the error in the location estimate is inproportion to the error in the cell coordinates.

In a Cell ID based location system, the location estimate may becalculated as a weighted average of the locations of the cells heard bythe mobile. The location estimate is thus corrupted in proportion to theerror in the cell location. In one example, a mobile radio terminal 20reports ten cells and one of those cells has a coordinate error placingit 1 km away from its true location. The effect of the error in thiscase after the averaging process is to move the location estimateapproximately 100 m in the direction of the error.

For other types of location systems, the effect of the error can varysignificantly depending upon the relative importance of the erroneouscell in the overall position computation. In some cases the erroneouscell is detected and substantially ignored and thus results in a minorloss of accuracy associated with having one less useful measurement. Inother cases the affected measurement may play a significant role inconstraining the solution; that is it has a disproportionate effect onthe location estimate. In such cases, the resulting location error canbe large and have a significant impact on the performance of associatedlocation-based applications.

The effect of cell location errors on a zone-based location system, suchas that described in PCT/AU2006/000478, may vary. Once the zone has beendefined, the location of the cell is typically less critical. During thezone registration phase there are, however, circumstances where celllocation errors may affect the performance of a zone-based system. Iffor instance such a system first computes a location estimate tovalidate the location of a zone registration request against a nominallocation, as described in PCT/AU2006/001479, then cell coordinate errorscould lead to the request being rejected as not being consistent withthe expected zone location.

A zone based system that incorporates predicted measurements into thezone definition will also typically be affected by cell location errors.For example, in a Cell ID based system zones are defined by the set ofcells that can be heard in the zone. These can be assigned bymeasurement, prediction or a combination of both. Cells that havecoordinate errors may be included in a zone where they don't belong,artificially creating a second zone. Conversely a cell with incorrectcoordinates may not be included in a given zone definition thusdegrading the performance of the zone.

For zones that include signal-strength as part of the definition, thesignal level predicted for a cell with incorrect coordinates may be inerror. The effect of the error can vary from slightly different zoneboundaries to significant zone performance problems such as the mobilebeing declared out of the zone when actually situated within the areaintended to be enclosed by the zone definition.

Other Cell Parameter Errors

There are other cell parameter errors that can affect location estimateand zone performance depending upon the types of measurements used bythe system.

Location systems that rely on signal strength can be impacted by errorsin antenna azimuth, antenna down-tilt, antenna characteristics such asgain and beamwidth, and effective transmit power levels. A cell that istransmitting at a higher power level than is stated in the networkdatabase typically will mean that a location system estimating range tothe cell using signal strength will place the mobile terminal closer tothe cell than it actually is. Similarly, errors in the parametersassociated with the antenna may degrade the location estimate.

Other location systems that do not rely as directly on signal strengthmay also be degraded by these types of parameter errors. For exampletiming-based systems may rely on antenna azimuth in order to associatetiming measurements with the corresponding cells where such cells werenot uniquely identified. Another example is a cell ID based system usingthe cell centroid. In that system it is necessary to know the antennacharacteristics and in particular whether the antenna is directive oromni-directional.

Process

There are various aspects to dealing with network databases with respectto location systems. These are: detection of inconsistencies between theas configured network and the network database; dealing with networkinconsistencies once they are detected; and updating the system inresponse to network configuration changes, corrections to the networkdatabase or in response to a detected inconsistency.

The following describes various processes for detection ofinconsistencies between the as configured network and the networkdatabase. The other two aspects are described in a co-pending patentapplication entitled “Mobile Service Maintenance Management” filedconcurrently herewith.

Detecting Network Inconsistencies

By inconsistency we mean any difference between the actual networkconfiguration and the representation of the network in the network. Aninconsistency can include an absence of in the database of a networkelement that is in the actual network, the presence of a network elementin the database that is not in the actual network, and/or a variation ina value of a network parameter in the database from that of the actualdatabase.

In certain embodiments of the present invention, the detection of suchinconsistencies is based on analysis of observations of the networksignals compared against the network database. Currently, radio networkoperators typically use dedicated equipment to detect inconsistencies inthe radio network. They commonly do so by fitting out vehicles withmobile radio receivers and GPS receivers to map the radio networksignals. In one example, taxis are used to provide spatial coverage andare on the move for significant periods of each day. For specificproblems the operators may deploy an instrumented test vehicle to surveythe area where there is a service problem.

The following describes various processes by which certain types ofinconsistencies can be detected. The different processes can be appliedin parallel. That is, a given set of observations can be applied to oneor more of the processes at the same time and/or in any order.Alternatively, each process may be applied one after the other, or acombination of both.

The observations of the network 10 can come from any receiver that ismonitoring the network. This includes for instance the large number ofmobile terminals 20 in everyday use. Such mobile radio terminals arecontinually making measurements of the radio network signals. One aspectof the present invention is to take advantage of existing measurementsand use those measurements to detect inconsistencies. This allows theoperator to leverage large numbers of temporally and spatially diverseradio measurements available from across the network at minimumadditional cost. In the context of a home zone solution, suchmeasurement data is included with zone registrations and locationrequests. As described herein, the measurement data, if desired, canalso be included with other messages from mobiles to the system. Thishas the advantage of utilizing spare capacity and/or existingcommunication sessions.

The processes by which inconsistencies are detected can be performed inreal-time or through data post-processing. Real-time processing refersto the processing of measurements to detect inconsistencies as soon aspractically possible after the measurements become available. Forexample the measurements would be placed in a queue and the systemprocesses the data in the queue as fast as the system can.Post-processing refers to the accumulation and storage of themeasurements for batch processing at a later time. For example thesystem could process the measurements when it was not processing moreurgent tasks

Detection Using a Single Measurement

Certain inconsistencies can be detected from a single measurement whichcontains data which is inconsistent with that held in a database.Certain inconsistencies can be detected from multiple measurementscontaining data which in combination is inconsistent with that held inthe database.

Depending upon the type of hypothesized inconsistency multiplemeasurements may be required in order to gain sufficient confidence toact upon the inconsistency.

The process according to one aspect of the present invention, ofdetecting an inconsistency from a single observation is illustrated inFIG. 3. The process begins at step 100. At step 101, measurements of oneor more parameters are obtained from the network. In steps 102 and 103,for each measurement, the measurement is compared with its correspondingdata in the database 50, or if the measurement is such that a metricshould be calculated, a metric is calculated from the corresponding datain the database. Such metrics described herein include but are notlimited to calculating the distance between two simultaneously reportedcells, computing the zone detection cost associated with a cell. In step104, a determination is made as to whether the measurement is consistentwith its corresponding data in the database. In the case of a calculatedmetric, this can be compared to a given threshold. If the measurement isconsistent with the database or the calculated metric is within thegiven threshold, the process ends at step 106. If the measurement or thecalculated metric are not consistent with the database, then thatmeasurement is marked or flagged as inconsistent. A given set ofobservations also referred to as measurements, of the network 10, insome forms, will contain data pertaining to one or more cells. For eachsuch cell the data observed is compared against that held in thedatabase or against a metric computed using data from the database. Ifthere is a difference between the observed data and the database, or thecomputed metric exceeds its associated threshold, then an inconsistencyhas been detected for that cell. All cells so identified from the set ofmeasurements are then passed on to other processes that take actions toresolve the inconsistencies as are described in the above-mentionedco-pending patent application dealing with management of the detectedinconsistencies. Specific uses of the process of detection using asingle measurement include the processes illustrated in FIGS. 5, 7, 8and 9 as will be described in more detail below with respect to eachfigure.

Detection Using Multiple Measurements

Inconsistencies can be also detected by accumulating evidence over timeor over a series of measurements to support a hypothesis. In particularthis is required for inconsistencies for which a single measurement doesnot provide sufficient confidence to conclude that the inconsistencyexists. An example is the detection of a cell being non-operational. Nothearing the cell in any given measurement set does not prove the cell isnot operating whereas the converse of presence of a cell in a set ofmeasurements proves that the cell is operating.

FIG. 4 illustrates a process flow for accumulating evidence to support agiven hypothesis where the hypothesis represents the existence of a typeof inconsistency. The process begins at step 200. In step 201, a set ofmeasurements are obtained from the network. In steps 202 and 203, foreach cell reported in a set of measurements, the observed parameters arechecked to see if the observation contains evidence that denies thehypothesis. As stated above, the presence of a cell in a set ofmeasurements denies for the time being the hypothesis that the cell isnon-operational. These steps are illustrated as steps 205 and 206 inFIG. 4. In the case where the measurement does not deny the hypothesis,the next step is to use the properties of a zone-based location systemto find and accumulate evidence to support the hypothesis. In steps 207and 208, for each zone, the process determines whether the mobile radioterminal 20 from which the measurements were obtained, is in the givenzone. If so, then in steps 209 and 210, for each cell in the zoneprofile of the given zone, when the mobile radio terminal is in a givenzone, there are known expectations of what cells are hearable, theexpected signal levels and variation, within that zone. Comparisonsbetween the measurements and the expected measurements from the databaseenable evidence to be gathered to support or deny the hypothesizedinconsistency. As an example, the process could be used to detect achange in the transmit power level by tracking the difference betweenthe expected and observed signal levels for each cell. If the evidencefor the hypothesized inconsistency exceeds a configured or predeterminedthreshold (step 211), the hypothesis is accepted and the cell is flaggedas being inconsistent in step 212. In step 213, all cells flagged asbeing potentially inconsistent are collected for subsequent processing.The process then ends at step 214. A particular example of an indirectdetection process using an accumulation of evidence is described in moredetail below with reference to detection of non-operational cells andFIG. 6.

For particular inconsistencies the step of checking measurements forevidence to deny a hypothesis can be left out. If there are no specificobservations that can deny the hypothesis then there is no value in thisstep. In the case of detecting non-operational cells, the observation ofa given cell is evidence that denies the hypothesis. In the case wherethe hypothesis is that a cell has changed transmit power levels thevariability of signal strength observations and relatively small changesmade to power levels that a single measurement cannot prove that therehas not been a change.

The process of accumulating evidence can also be applied toinconsistencies that are detectable based on a single measurement andhence would be suitable for detection via a direct detection process asdescribed above. In particular, if there is the possibility that anygiven measurement may be erroneous and provide a false indication of aninconsistency, the evidence accumulation process can be used to make theprocess more robust. In one example, there is a GSM mobile that iscamped on cell 6612 and is also hearing a cell on ARFCN 61 with BSIC 56which resolves to cell 4459 based on proximity to cell 6612. A dataerror on 1 bit results in the same cell being reported in a differentset of observations as ARFCN 61 with BSIC 57. Within the proximity ofcell 6612 this ARFCN/BSIC combination is not found in the database andthus the observation appears as an inconsistency. A threshold could beset such that the inconsistency is only flagged for attention if thesame inconsistency is observed one or more times. In certain embodimentsthe inconsistency detection threshold may be set to between about 1 toabout 50 times, or between about 1 to about 10 times, or between about 1to about 5 times, or between about 1 to about 3 times. In one particularexample, there is a system with the threshold set at 3 observations.Continuing the previous example the observation of ARFCN/BSIC 61/57would need to be observed in 3 separate observations before theinconsistency would be considered to be evidence of a new cell. This isillustrated in Table 1 below.

TABLE 1 Unknown Inconsistency Serving Cell ARFCN/BSIC Count Reported6612 61/57 1 No 2186 61/57 2 No 6612 61/57 3 Yes

A further refinement would be to make the criteria include the servingcell. Continuing the above example the ARFCN/BSIC 61/57 would need to beobserved with cell 66123 times before the inconsistency would be treatedas real. This avoids the problem of the same inconsistency from distinctgeographical locations being treated as evidence of the sameinconsistency. This example is illustrated by expanding upon Table 1whereby the observation with serving cell 2186 is treated as a separateinconsistency with a separate incident count This is illustrated inTable 2 below.

TABLE 2 Unknown Inconsistency Serving Cell ARFCN/BSIC Count Reported6612 61/57 1 No 2186 61/57 1 No 6612 61/57 2 No 6612 61/57 3 Yes

The trade-off is certainty of decision versus time delay in reportingthe inconsistency. The more certainty required, for example a highercount threshold, the longer it will take to gather sufficientobservations. If a measurement indicates that a previously observedinconsistency is no longer present, the evidence is reset, for examplethe counter would be set to 0.

Optionally a timer could be associated with an inconsistency such thatit must be detected a number of times within a time window beforeconcluding that the inconsistency exists. When an inconsistency isdetected the past history of the detections is examined over theconfigured time window and the number of incidents counted. If the countequals or exceeds the threshold, then the conclusion is that theinconsistency exists. This approach has the advantage of inconsistenciescaused by data errors which are expected to be rare events fromaccumulating over a long period and eventually causing an inconsistencyto be falsely concluded to exist reported. The time window can beconfigurable. Time windows could vary over a number of ranges of time,for example, from about 0 to about 24 hours, or from about 0 to about 1hour, or from about 0 to about 15 minutes, or from about 0 to about 6hours, or from about 0 to about 48 hours, or from about 10 seconds toabout 3600 seconds, or from about 10 seconds to about 300 seconds.

Table 3 illustrates the method by extending the example from table 1.The time, in the example measured in seconds from an arbitrary epoch, atwhich an inconsistency is observed is noted. The number of occurrencesof the same inconsistency within the configured time window, in thisexample 300 seconds, is counted each time the inconsistency is detected.If the threshold is reached within the time window, the inconsistency istreated as real. In the example the threshold is 3 and this threshold isreached within a 300 second window at time 23872 seconds. It should beclear to one of ordinary skill in the art that the inconsistencycriteria can be extended to include the serving cell as described aboveand illustrated in Table 2.

TABLE 3 Count Serving Unknown Time over 300 Inconsistency CellARFCN/BSIC (seconds) seconds Reported 6612 61/57 12876 1 No 2186 61/5723611 1 No 6612 61/57 23732 2 No 6612 61/57 23872 3 Yes

In certain embodiments, the window may be defined in terms of the numberof opportunities presented to the system in which the inconsistency maybe detected rather than in terms of elapsed time. An example of anopportunity to observe an inconsistency is the receipt of a message froma mobile by the server where the message contains radio measurements asdescribed herein. In some instances, the system may be configured torequire the number of detections to exceed a threshold and for thatnumber of detections to occur within a number of opportunities.

The detections threshold may be set to between about 1 and about 5,between about 1 and about 20, between about 1 and about 100, betweenabout 3 and about 10, between about 5 and about 50, between about 20 andabout 100, or between about 50 and about 1000. The opportunitiesthreshold may be set to between about 100 and about 10,000,000, betweenabout 200 and about 1000, between about 500 and about 2000, betweenabout 1000 and about 5000, between about 2000 and about 10,000, betweenabout 5000 and about 20,000, between about 10,000 and about 50,000,between about 20,000 and about 100,000, between about 50,000 and about200,000, between about 100,000 and about 500,000, between about 200,000and about 1,000,000, between about 500,000 and about 2,000,000, betweenabout 1,000,000 and about 5,000,000, or between about 2,000,000 andabout 10,000,000. As an example, consider a network with 2000 cells andon average each message containing radio measurements contains 6 cells.On average a cell indicating an inconsistency will only be present onceevery 333 messages. To be reasonable confident that a reportedinconsistency is valid and to provide a reasonable likelihood that theinconsistency will be detected the criteria could be specified as 3detections within 2000 opportunities.

Detecting Unknown and Incorrectly Identified Cells

FIG. 5 illustrates a process flow for detecting the presence of a cellthat is not in the database or for which one or more cell identificationparameters does not match that in the database.

Starting at step 300, the process takes one or more measurements at step301 of the network signals and compares for each measurement the cellidentity information against that in the database as shown in steps 302,303 and 304. Using one or more of the techniques described elsewhere inthis specification the cell identifiers included in the measurements arechecked.

If the result of this processing indicates that the cell identifiers arenot in the network database, the cell is flagged as unknown in step 307.If the result of this processing indicates that the cell identifiers areknown, then a check is made in step 305 to see if the known cellidentifiers are consistent with the network database. If not, then thecell is flagged as inconsistent in step 306. In step 308, all of theflagged cells are gathered together for subsequent processing accordingto one or more methods of the co-pending patent application referred toabove relating to management of detected inconsistencies. This processends in step 309.

The following provides detailed examples of carrying out various aspectsof the above method.

Detecting Unknown Cell Based on Unique Identifier

The observation of a cell ID that is not present in the network databaserepresents an inconsistency between the as configured network and thenetwork database. The unknown cell ID could represent a cell recentlymade operational but not in the database, a cell prematurely removedfrom the database, or a cell assigned the wrong ID in the network or inthe database.

One example is a GSM system in which a new base station site isinstalled. At this site there are three cells with parameters as shownin Table 4 below. The data associated with these new cells has not beenupdated into the network database. A set of measurements is shown inTable 5 below. The system will detect the Cell ID 25071 as an unknowncell as the Cell ID will not be present in the database.

TABLE 4 Cell ID ARFCN BSIC 25070 95 38 25071 81 59 25072 67 46

TABLE 5 Cell ID ARFCN BSIC RxLev Mean (dBm) 25071 Unknown Unknown −83.0Unknown 71 61 −92 Unknown 67 46 −99 Unknown 69 43 −103 Unknown 73 34−103

Based on the example for GSM it will be clear to those of ordinary skillin the art how to apply the method to other radio access technologiessuch as UMTS, CDMA-2000, and CDMA IS-95.

Detecting Unknown Cell Based on Non-Unique Identifier

Base stations that have only been identified by partial identifiers areassociated with cells by searching for a match to the partial identitywithin the vicinity of the serving cell. Failure to find a match for thepartial identity indicates an inconsistency between the as configurednetwork and the network database. The failure, however, cannot beattributed to a specific cause. Potential causes include, but are notlimited to, a new cell in the network; premature removal of a cell fromthe database; incorrect partial identifier information in the database;incorrect coordinates for the serving cell, and incorrect coordinatesfor the neighbour cell.

One example is a GSM system in which a new base station site isinstalled. At this site there are three cells with parameters as givenin Table 1 above. The data associated with these new cells has not beenupdated in the network database. A set of measurements is shown in Table6. The system will attempt to identify the unique identity of theneighbour cells that are partially identified by Cell ID and BSIC. Asearch within the vicinity of Cell 26078 fails to find a match for theARFCN/BSIC pair of 81/59. The failure to match the identity of thismeasurement indicates the presence of an unknown cell.

TABLE 6 Cell ID ARFCN BSIC RxLev Mean (dBm) 26078 Unknown Unknown −87.0Unknown 71 61 −92 Unknown 81 59 −99 Unknown 69 43 −103 Unknown 73 34−103

Where a system or network element, for example a mobile radio terminal,contains a partial copy of the network database, inconsistencies can bedetected in some circumstances where the observed identifiers contradictthe data that the mobile contains. When partial identifiers are reportedin conjunction with a unique identifier, the partial identifiers can becross-referenced against the unique identifier. For example, if themobile radio terminal contains a reference to GSM Cell ID 24141 withARFCN 59 and BSIC 51 but observes Cell ID 24141 on ARFCN 32 and BSIC 27then an inconsistency has been detected.

Based on the example for GSM given above, it will be clear to those ofordinary skill in the art how to apply the method to other radio accesstechnologies such as UMTS, using UARFCN and PSC and CDMA IS-95 using thechannel number/PN offset.

Detecting Unknown Cells Via Zone Detection

A mobile radio terminal will not necessarily store the entire networkdatabase and hence may not be able to identify all of theinconsistencies discussed above. One aspect of this invention is amethod for a mobile radio terminal to alert the server 30 to thepossibility of there being an inconsistency between the as configurednetwork and the network database. In a zone-based location system themobile contains one or more zone profiles and each such profile containsa subset of the network database. The zone detection process evaluatesthe difference between current measurements and that expected to be seenin a given zone as defined by the profile for that zone. Eachmeasurement makes a contribution to the decision as to whether themobile is in a given zone or not. If a single measurement is responsiblefor a significant portion of the decision metric, then the computationis repeated with that cell removed. If as a result the mobile radioterminal is deemed to be within the zone, the cell associated with themeasurement is flagged as representing an inconsistency. An indicativeportion for a measurement's influence to be deemed significant is 40% ofthe decision metric's value. The proportion of the cost deemedsignificant could be in the range of about 10% to about 80%, about 10%to about 30%, about 15% to about 40%, about 20% to about 50%, about 30%to about 60%, about 40% to about 80%. The technique is particularlyuseful for detecting newly commissioned cells and errors in theconfiguration of the network that have arisen during a network retune.

TABLE 7 Cell ID ARFCN BSIC RxLev Mean Sigma 25068 95 38 −60.0 9 54763 8159 −88.3 9 18322 67 46 −92.1 9 892 71 61 −98.7 9 18581 73 34 −103 9

A GSM radio zone profile is given in Table 7. In this example, a new setof measurements is available as illustrated in Table 8 below and thesewill be used to evaluate the zone status against the profile from Table7. The ARFCN and BSIC are not available for the serving cell becausethey are not reported in the Network Measurement Report (NMR) data.

TABLE 8 Cell ID ARFCN BSIC RxLev (dBm) 49844 Unknown Unknown −83 Unknown95 38 −89 Unknown 81 59 −90 Unknown 71 61 −92 Unknown 67 46 −99 Unknown69 43 −102

The total cost is calculated as described in PCT Patent Application No.PCT/AU2006/000478 by summing the costs corresponding to the matched,unmatched and unreported cells. The calculated values for the matchedcell costs are shown in Table 9, represented to 2 decimal places.

Profile Measured Cell ID ARFCN BSIC RxLev RxLev Cost 25068 95 38 −85.0−89 0.20 54763 81 59 −91.3 −90 0.02 892 71 61 −98.7 −92 0.55 18322 67 46−92.1 −99 0.59

The calculated value for the single unreported cost is shown in Table10. In this example the unreported cell is not included in the costbecause it would not be expected to be heard given the levels that theother signals were reported at.

TABLE 10 Profile Cell ID ARFCN BSIC RxLev Threshold Cost 18581 73 34−103 −102 0.00

The calculated value for the unmatched costs is shown in Table 11.

Measured Cell ID ARFCN BSIC RxLev Threshold Cost 49844 Unknown Unknown−83 −103 4.94 Unknown 69 43 −101 −103 0.05

The total cost is 6.35. The cost of the cell 49844 represents 78% of thecost. This exceeds the indicative threshold of 40% so the test isperformed. The cell is not included in the zone detection test. Thetotal cost is now 1.41. The threshold at the 80% probability level,derived from a chi-squared distribution with 5 degrees of freedom, is2.34. The cost is less than this threshold and thus excluding the cellwith the large cost would result in the mobile being declared in thezone. Thus the cell 49844 is declared to be a new cell. The same processcan be used to identify the presence of a new cell that was onlyidentified by non-unique identifiers (ARFCN+BSIC). In this instance thepresence of the unknown cell would be detected but its unique identitywould not be known.

A mobile radio terminal can only measure cells that are in its vicinity.Base station coordinate errors may be detected by identifyingmeasurement sets that are incongruous. Various metrics can be used toevaluate the likelihood that a cell's coordinates in the database areincorrect or that a given set of measurements contains one or more cellswith suspect coordinates.

Detecting Non-Operational Cells

In any given measurement set, the existence of a cell constitutes proofthat the cell is operating. The converse is not true; the absence of agiven cell in a given set of measurements does not necessarilyconstitute proof that the cell is not currently operating. According toone aspect of the present invention, a process to detect cells suspectedof not operating is to accumulate evidence from a series of one or moresets of measurements until such time as a decision can be made about theoperational state for a given cell.

FIG. 6 illustrates a process flow for detecting cells that are in thenetwork database 50 but which appear to be non-operational. The processbegins at step 400 to obtain measurements from the network at step 401.For each cell that is observed in a measurement set, the cell is deemedto be operating and hence the evidence that it is not operational isreset to 0 at steps 402, 403 and 404 by setting Cell Cost=0 and CellCount=0.

If the mobile radio terminal 20 is in a zone, then there exists theopportunity to update the evidence that cells that have not beenreported may be non-operational. From step 405 the following steps aretaken for each zone being considered. A zone is a region within themobile radio communications network 10 that may be defined by variousmeans, including those described in PCT/AU2006/000478 entitled “EnhancedTerrestrial Mobile Location”. In step 406, a determination is made as towhether the mobile radio terminal 20 is in the zone. Again, variousmethods may be used to determine whether the mobile radio terminal is inor out of the zone, including those described in detail in this sameincorporated reference. In steps 407 and 408, it is determined for eachcell in the zone profile, whether the cell was reported. If not, theevidence that the cell is not operating is updated in step 409 bycomputing the unreported cell cost and adding this to the accumulatedcell cost and incrementing the cell count. If the cell is determined tobe reported, the process ends at step 413.

In step 410, the collected evidence is compared with a threshold. Inthis case, if the accumulated unreported cell cost is greater than thethreshold for the cell count (i.e. the threshold is exceeded), the cellis flagged as potentially non-operational in step 411. In step 412, alcells that have been flagged as potentially non-operational arecollected for further processing

The following sections provide detailed examples for detectingnon-operational cells

Detecting Non-Operational Cells at Server Based on Unreported Cost.

In one aspect of the present invention, a means and method is providedfor detecting that a particular network cell is out of service. This isdone using the measurements observed by one or more mobile radioterminals. The detection process uses a metric reflecting theexpectation of having not observed a cell given the other cells thatwere observed. With each set of measurements the metric is accumulatedfor each cell. A cell is flagged as non-operational when the accumulatedmetric is evaluated and deemed to contain insufficient evidence that thecell is currently operating. For each cell present in a set ofmeasurements the metric accumulator is reset.

Different metrics can be used to determine whether a cell is operationalor not. For a given measurement set the probability of not reporting agiven cell given the observed signal measurements and assuming that thecell is operating can be calculated. These probabilities can then beaccumulated for a given cell over a series of measurements bymultiplying the probabilities. If the accumulated probability crosses athreshold, for example 0.005, the cell is flagged as non-operational.When a cell is present in a measurement set, the accumulated probabilityis reset to 1.

Another metric is to compute a statistic such as the chi-squaredstatistic, for each cell and accumulating the statistic by adding thevalues over a series of measurements. When the accumulated statisticexceeds a defined threshold the cell is deemed to be non-operational. Ifthe cell is present in a given measurement set the statistic is reset to0.

Yet another metric is to accumulate the time since a cell was lastreported. A cell that has not been included in a measurement set forlonger than the configured interval is flagged as non-operational.

Yet another metric is to accumulate the number of measurement sets thathave been processed since a cell was last reported. A cell is flagged asnon-operational if it has not been reported within the most recent nmessages. Such a metric avoids the false alarms that time-based metricscan generate during quiet periods such as the early morning.

In one embodiment for a zone-based location system such asPCT/AU2006/000478, the process at the server operates on all cells inthe network which feature in one or more zone profiles. For each ofthese cells the unreported cost is accumulated each time a new set ofmeasurements yields a large unreported cost but the remaining profileelements yield a good match for the measurements. The general process isto examine each cell that relative to a given zone profile is notreported in a given set of measurements. If the cost of a givenunreported cell is deemed significant and when this cell is ignored theremaining measurements indicate that the mobile is in the zone, then thecell is deemed potentially non-operational and the evidence to supportthis is accumulated.

It is common for a site to be non-operational and consequently all cellslocated at that site would be non-operational. The reasons for this aredue to the resources shared by all cells at a site; in particular powerand data. It should be clear to those of ordinary skill in the art thatthe algorithm described can also be applied on a site basis. In thismode of operation the unreported cost is accumulated on a per sitebasis.

For each of these cells the unreported cost is accumulated each time anew set of measurements yields a large unreported cost but the remainingprofile elements yield a good match for the measurements. In physicalterm, such an observation indicates that the mobile radio terminal islocated in a place where the measurements are consistent with theprofile but this particular cell is not reported. Each time a cell isreported however, the accumulated cost is reset to zero since the cellis clearly not in an outage state.

The following description illustrates a scenario based in a GSM networkin which a measurement yields a large unreported cost, such that a celloutage may be indicated.

This example uses the profile defining a zone as shown in Table 12.

TABLE 12 Cell ID ARFCN BSIC RxLev Mean Sigma 25068 95 38 −60.0 9 5476381 59 −88.3 9 18322 67 46 −92.1 9 892 71 61 −98.7 9 18581 73 34 −103 9

In this example, a new set of measurements is available as illustratedin Table 13 below. The ARFCN and BSIC are not available for the servingcell because they are not reported in the NMR data.

TABLE 13 Cell ID ARFCN BSIC RxLev Mean (dBm) 54763 Unknown Unknown −83.0Unknown 71 61 −92 Unknown 67 46 −99 Unknown 69 43 −103 Unknown 73 34−103

The total cost is calculated as described in previously-incorporated PCTPatent Application No. PCT/AU2006/000478 by summing the costscorresponding to the matched, unmatched and unreported cells. Thecalculated values for the matched cell costs are shown in Table 14,represented to 2 decimal places.

TABLE 14 Profile Measured Cell ID ARFCN BSIC RxLev RxLev Cost 54763 8159 −88.3 −83 0.17 892 71 61 −92.1 −92 0.00 18322 67 46 −98.7 −99 0.0018581 73 34 −103 −103 0.00

The calculated value for the single unmatched cost is shown in Table 15.

TABLE 15 Measured Cell ID ARFCN BSIC RxLev Threshold Cost Unknown 69 43−103.3 −105 0.02

In this example, since the measurement was not fully populated, usingthe methods described in previously-incorporated PCT/AU2006/000347(referred to above and herein incorporated by reference in itsentirety), an unreported threshold value of −105 is used. The calculatedvalue for the unreported cell cost is shown in Table 16:

TABLE 16 Profile Cell ID ARFCN BSIC RxLev Threshold Cost 25068 95 38−60.0 −105 15.07

A suitable threshold at which an unreported cost is consideredsignificant enough to be accumulated for outage detection is about 2.0for example. However, any other suitable thresholds could be used, suchas between 0 and 0.5, 0.2 and 1.5, 1.0 and 3.0 or between 1.5 and 5.0 orbetween 2.0 and 6.0 for example.

Having determined that there is an unreported cell for which thethreshold has been exceeded, there needs to be a determination that thecell was expected to be heard before the cost can be accumulated asevidence that the cell is not operating. Using the process described inpreviously-incorporated reference PCT/AU2006/000478, the zone status iscalculated but with the unreported cell in question not considered inthe computation. The threshold which the other costs must not exceed inorder for the unreported cost to be accumulated is defined in terms ofthe chi-squared threshold in the same way as for the zone status, takinginto account the number of constraints (not counting the unreportedconstraints). In the present example, the total number of otherconstraints is 5. The chi-Sq threshold value is obtained as the 90thpercentile from the ChiSq cumulative density function with 5 degrees offreedom. Using a numerical approximation to this function, rounded to 1decimal place, the value is 9.2. The total of matched and unmatchedcosts is 0.19 which is less than the chi-sq value of 9.2.

In the example above, the mobile is determined to be in the zone if theunreported cell is ignored. This combined with an unreported cost thatexceeds the threshold means that the unreported cost is accumulatedtowards the cell being declared non-operational. The cost is accumulatedby adding the current, unreported cost to the accumulated cost. Once theaccumulated cost exceeds a threshold, the cell is declarednon-operational. Any time the cell is observed, the accumulated cost isreset to 0 since the cell is clearly operational. A suitable thresholdfor the total accumulated unreported cost threshold before declaring anoutage to be indicated is about 20 for example. However, any othersuitable threshold may be used, including between 10 and 15 or between15 and 30 or between 20 and 40 for example.

Detecting Non-Operational Cells at Mobile Terminal

A limited scale version of the process illustrated above may also beoperated at each mobile terminal. In this case however the outageanalysis at a mobile terminal focuses only on the cells that feature ina zone profile being monitored at that terminal. In this case ahistorical unreported cost is maintained for cells included in such azone profile. In the event that for a particular profile, all but a fewelements are matched and the remaining elements attract a largeunreported cost, these unreported costs may be accumulated.

When the accumulated unreported cost for one or more cells reaches thethreshold, a message may be sent to the server bearing the current radiomeasurements. The purpose is to trigger the server side cell outagedetection processing using the current measurements and potentiallytrigger the disabling of that cell.

This mobile radio terminal focused aspect may be useful in some casesbecause a mobile radio terminal may return to a zone after a cell hasbeen taken out of service. The lack of measurements for that cell mayprevent the mobile from ever detecting itself as home and thereforeprevent any radio measurements being sent to the server. As a result,the server would never have data from which to detect the outage and thezone service will be interrupted. By performing this limited outageanalysis at the mobile radio terminal, it is possible to detect suchcases and activate the outage processing in the server.

Detecting Non-Operational Cells in Server

The elapsed time since a cell was last reported can be used to establishwhether a cell is believed to be operational or not. In one aspect ofthe invention, the time at which a cell was last reported is associatedwith each cell enabling the elapsed time since last seen to becalculated for every cell at any given epoch. Any cell for which theelapsed time since last report exceeds a specified threshold is deemedto be non-operational. The threshold is optionally configurable. Thethreshold chosen represents a trade-off between responsiveness and falsealarms. The larger the time before reporting a cell as non-operational,the less likely it is that the report is a false alarm. Indicativevalues for the threshold are between 1 minute and 5 minutes, between 2minutes and 20 minutes, between 5 minutes and 60 minutes, between 15minutes and 120 minutes.

The threshold can optionally change based on the time of day to reflectthat the expected time between reports will be longer when there is lesspeople movement such as early in the morning. If the rate ofobservations varies significantly throughout the day or by day-of week,a more appropriate threshold is one based on the number of elapsedtransactions since the last observation rather than the elapsed time. Aseach transaction arrives it is assigned an value that is incrementedwith each transaction. Each cell is then assigned the value of the mostrecent transaction in which it was observed. Once the number oftransactions since a given cell was last observed is exceeded it isdeemed non-operational. The threshold is again a trade-off betweenresponsiveness to a cell becoming non-operational and false alarms.Consider a network with 3000 cells and on average 6 cells are reportedper set of observations. In such a network a minimum 500 sets ofobservations are required for every cell to have the possibility ofbeing reported once. Taking into account the random nature of whichcells are reported, a reasonable value is 3 times the minimum settingthe threshold at 1500.

In another aspect of the invention, elapsed time is measured relative tothe rate of transactions coming into the location server. The locationserver maintains a transaction counter. Associated with each cell is thetransaction counter value associated with the transaction in which thecell was last detected. The elapsed time since a cell was last reportedis measured as the difference between the current transaction countervalue and that stored for the cell. A given cell is deemednon-operational if the number of transactions since last update exceedsa specified threshold. Optionally the threshold is configurable. Forexample in a network with 2000 base stations and a GSM network in whichmobile reports at most 7 cells at any time, it would take approximately300 messages to see each cell reported once ignoring the randomness ofsuch reports. The threshold could be set at 3000 to allow for the randomdistribution of which cells were reported. Using the elapsed transactioncount metric has the advantage of adapting to the rate at whichtransactions are being gathered and hence automatically handles theperiods where the actual elapsed time is expected to be larger due tofewer incoming transactions.

In another aspect of this invention, the server can optionally seek toobtain further evidence that a cell is non-operational by requestingcertain mobiles send measurements to the server. If a given cell issuspected of having failed, the server can search zone profiledefinitions to find zones which include the suspected cell. Optionallythe server could prioritise the list of zones based on the signalstrength order of the zones. Zones where the suspect cell is expected tobe highest are given preference where the suspect cell is second highestand so on. From this zone list the server seeks mobile radio terminalsthat are in the zone. Such mobile radio terminals, optionally based onzone preference, are then requested to send a set of measurements, forexample by forcing a status update. The number of mobiles so targeted isconfigurable.

Detecting Non-Operational Cell Becoming Operational

FIG. 7 illustrates a process flow for detecting cells that have beenflagged as non-operational (for example, by one or more of thepreviously-described methods), but have been re-activated. Whenever acell that has been flagged as non-operational is observed in ameasurement set the cell is flagged as being operational. The processstarts from step 500 to collect measurements from the network at step501. For each reported cell, the operational status is checked at steps502 and 503. If in step 504, the cell has been flagged asnon-operational (for example by the previously-described method), thecell is then flagged for reporting in step 505. If the cell has not beenflagged as non-operational, no further action is taken.

In step 506, the cells flagged in step 505 are gathered together forpotential reinstatement as operational. The process then ends in step507.

The following provides a detailed example of performing the abovedescribed method.

The server can detect the reappearance of a non-operational cell usingthe same algorithms used to detect non-operational cells. Whenmonitoring the network for non-operational cells the presence of a celldeemed non-operational in a set of measurements indicates that the cellis operating again.

As an example consider a set of measurements made on a GSM network asshown in Table 17. Using the network database and proximity to the cell25652 the cell with ARFCN 68 and BSIC 51 is resolved to be cell ID54312. This cell is flagged in the server database as non-operational(Table 18). The detection of cell 54312 infers that it is againoperational and consequently the operational status is changed.

TABLE 17 Cell ID ARFCN BSIC RxLev Mean (dBm) 25652 Unknown Unknown −87.0Unknown 56 66 −94 Unknown 61 54 −95 Unknown 68 51 −95 Unknown 29 46 −102

TABLE 18 Cell ID ARFCN BSIC Operational 25652 31 39 Yes 38821 56 66 Yes49731 61 54 Yes 54312 68 51 No 54311 29 46 Yes

Detecting Cells with Incorrect Coordinate

FIG. 8 illustrates a process flow for detecting cells that haveincorrect coordinates. From step 600, the process obtains the networkmeasurements in step 601. In step 602, the process determines thedistance between all pairs of cells that have been measuredcontemporaneously based on the coordinates of the cells in the networkdatabase. For each cell a metric relating to the relative proximity ofthe cells is computed and if the metric exceeds the criteria asdetermined in steps 603 and 604, then the cell is flagged in step 605 aspotentially having incorrect coordinates.

In step 606, the flagged cells are collected for subsequent furtherprocessing, and the process ends at step 607.

The following provides detailed examples of performing various aspectsof the above method for detecting a cell with incorrect coordinates.

A mobile radio terminal can only measure cells that are in its vicinity.Base station coordinate errors can be detected by identifyingmeasurement sets that are incongruous. Various metrics can be used toevaluate the likelihood that a cell is in the incorrect location or thata given set of measurements contains one or more cells with suspectcoordinates.

Detection Using Distance Metric

The cells measured by a mobile radio terminal typically come from thesame geographic area. As such, the average or median distance from eachcell in a measurement set to the other cells in the set should becomparable. A cell having a distance metric much higher than the othersmay be an indication of a cell with a coordinate error. One such metricthat may be used is the median distance.

In one embodiment, for each cell the median distance to the nearest ncell sites is computed. As an example, n could be 8, although any valuein the range of about 2 to about 20, about 2 to about 5, about 3 toabout 8, about 4 to about 12, or about 2 to about 8, could be used. Anycontemporaneously reported pair of cells that is more than m times theaverage of the two median distances apart is deemed to indicate a cellpotentially in the incorrect location. As an example m could be 2although any value in the range of about 1 to about 20, about 1 to about3, about 2 to about 5, about 3 to about 8, or about 5 to about 20 couldbe used.

Table 19 shows a section of a network database with the median distancefrom each base station to the nearest 8 base station sites using ametric for the separation of base stations in the vicinity of each basestation. A set of contemporaneous measurements reports cell IDs 26078and 4415. The distance between these cells is 2002 m. This distance isunder the median distance for both cells so the measurement provides noindication of an incorrectly located base station. A different set ofcontemporaneous measurements reports cell IDs 26078 and 5617. Thedistance between these cells is 18006 m. This metric is 2 times thelarger of the median inter-site distances involved which is 11202 m.Hence the measurement indicates that a cell may have incorrectcoordinates.

TABLE 19 Median Distance to Cell ID Nearest 8 Sites (m) Easting Northing26078 5601 6495885 2662920 5617 4321 6478631 2673922 8173 7840 64828142672664 4415 3400 6493345 2663641

Detection Using Signal Detection Likelihood

Where a set of measurements contains more than one unique cellidentifier, cell coordinate problems can be detected by evaluating ametric that measures the likelihood that all such identified cells couldbe contemporaneously heard at the reported signal strengths at a givenlocation in the network. By evaluating this metric over the networkcoverage area the maximum likelihood can be found. The maximumlikelihood is compared against a threshold. If the likelihood is below athreshold, then the set of observations indicates that there is apotential problem with the location of one or more cells.

FIG. 9 illustrates steps of a method for determining the detection of acell in the wrong location using a probability metric. The processbegins at step 700, to obtain network measurements at step 701. In step702, a probability that each reported cell is in the correct location iscomputed (described in more detail below). For each cell (step 703), acomparison is made between the computed probability and a threshold instep 704. If the probability is less than the threshold, the cell isflagged as being potentially in the wrong location in step 705. In step706, the flagged cells are gathered together for subsequent processing.The process then ends in step 707.

For any given location x, the expected signal strength at x for eachcell can be estimated using techniques well known in the art such as theHata model (see section 2.7 of Mobile Radio Communications). Thedifference between the measured signal strength and the estimated signalstrength is affected by the difference between x and the true locationof the mobile radio terminal, the accuracy of the cell location, theaccuracy of the model, and the variability of signal strengthmeasurements. Using optimization techniques well known in the art themaximum likelihood can be estimated. The point x at which this occurs isthe maximum likelihood estimate of the mobile radio terminal's location.This location is not required in this instance as it is the maximumlikelihood value itself that is the quantity of interest. It is wellknown in the art that if only two cells are identified, the locationestimate is ambiguous; there will be two equally likely locations atwhich the cost is minimised. This is not relevant in the problem beingaddressed in this example—it is simply an artifact of the process. Towhich location the algorithm converges does not matter as it is theminimized cost and not the location that is of interest.

For a signal strength model using Gaussian errors, it is well known inthe art that the maximum likelihood calculation is equivalent to findingthe location x that minimizes the following equation:

$\chi^{2} = {\sum\limits_{i}\frac{\left( {S_{i} - {f_{i}(x)}} \right)^{2}}{\sigma_{i}^{2}}}$

Where

S_(i) is the measured signal strength for cell i, f_(i)(x) is theestimated signal strength at x for cell i, and σ_(i) ² is the varianceof the signal strength for cell i due to the type of radio environment.The measurements S_(i) are contemporaneous. Ω² is the cost that isminimized and is a chi-squared statistic for which the number ofdegrees-of-freedom is the number of cells heard. The Ω² statistic isconverted to a probability and it is this probability that is comparedto the threshold. If the threshold is exceeded, then the scenarioindicates that one or more cells involved potentially have a coordinateerror. The threshold is configurable and is a trade-off between reliablydetecting coordinate errors and the number of false alarms. Since thecoordinate error is static, the detection threshold can be setreasonably large to reduce the number of false alarms. Such a thresholdwill simply increase the expected time it will take for a given error tobe detected. The threshold may be set to any desired value. In oneexample, the threshold could be in the range about 95% to about 99.99%,and including about 96%, 97%, 98%, 99% or 99.5% or about 99.99%. Thethreshold may even be set lower than 95%, for example in the range fromabout 70%-about 90% or about 80%-about 95%. Having determined that oneor more cells are potentially in the wrong location, the next step is todetermine which cells to flag for further action. The simplest choice isthe default case where all cells involved are flagged as potentiallybeing in the wrong location and the problem of identifying which, isleft for an external system, for example a network operations team.Another choice is based upon the examination of the cost that each cellcontributes to the total and if it exceeds a threshold, it is flagged asbeing potentially in the wrong location. The ability of this approach todetect the cell at fault improves with the number of cells included inthe computation. As described above, the cost will be a Ω² statistic butwith one degree of freedom. Again the statistic is converted to aprobability and compared against a threshold probability, for example,98%, or any other ranges as described above. As described elsewhere inthis specification, evidence can be accumulated over multiplemeasurement sets and the decision based upon the accumulated evidence.In this scenario, the Ω² cost contribution for each cell can beaccumulated and compared against a probability threshold.

The method used to detect cells in the wrong location using measuredsignal strengths can also be applied to timing observations. Given thedescription of the method above it should be clear how to apply themethod to timing measurements.

Table 20 shows part of a network database. Table 21 shows an excerpt ofa set of measurements that illustrate cells A and B heardcontemporaneously, as shown in FIG. 10A. The signal levels have a rangecorrespondence, using the Hata model, of 2415 m and 4930 m respectivelyfor cells A and B. Circular arcs centred on A and B using these rangesintersect at two distinct points P and P′. At either of these two pointsthe cost function evaluates to 0 which is clearly less than any chosenchi-squared threshold. As such, there is no evidence that either cellhas a significant coordinate error.

Table 22 shows an excerpt of a set of measurements that illustrate cellsA and C heard contemporaneously, as illustrated in FIG. 10B. Notehowever that the true location of the cell is distinctly different tothat in the database which the following analysis will reveal. Thesignal levels have a range correspondence, using the Hata model, of 2415m and 203 m respectively for cells A and C. Circular arcs centred on thecoordinates of A and C, as defined in the database using these ranges,do not intersect. Using numerical optimization techniques well known inthe art, the point at which the cost is minimized is determined andshown as point X in FIG. 10B. For a standard deviation of 8 dB,appropriate for a suburban radio environment, the minimum cost is foundto be 19.02. The chi-squared threshold for 99.9% is 14.1. Thus the costexceeds the threshold and the observed signal strength is deemed to havearisen from variations due to noise. Thus either both of the cells aredeemed to potentially have a coordinate error. Further measurement setsinvolving A or C would indicate which was the more likely to be inerror. Note that had the coordinates for C been correct in the database,the circular loci would not intersect, at the optimal estimate X′ asshown in FIG. 10C. The resulting minimized cost would be 0.28, wellbelow the threshold.

It should be clear to those of ordinary skill in the art that thetechnique can be extended to situations where multiple signals areuniquely identified. The more signals so available makes the techniquebetter able to distinguish which cell actually has the wrongcoordinates.

TABLE 20 Cell ID Easting Northing A (in 455161 6654541 DB) B (in 4578326654541 DB) C (in 478751 6651368 DB) C (True) 455511 6651368

TABLE 21 Cell ID RxLev Mean (dBm) A −84.4 B −96.7

TABLE 22 Cell ID RxLev Mean (dBm) A −84.4 C −46.5

Detection Based on Zone Location

Described in PCT/AU2006/001479, is the association of a nominal locationto a zone. Similarly when a zone is measured, the measurement data canalso be used to estimate the location of a zone. Either of theselocations can be used to assist the detection of a cell with incorrectcoordinates.

The methods described earlier detect the presence of potential cellcoordinate errors with no prior information pertaining to the locationwhere the measurements were made. Using the zone location, theevaluation metrics can be further refined. If measurements are known tobe made within the vicinity of a zone with a known location, for examplethe measurements were triggered via a zone transition, then themeasurements can be evaluated assuming they came from the location ofthat zone.

Distance Metric

With regard to the distance metric, the distance from the zone locationto each cell can be computed. This distance is then compared to amultiple of the inter-cell distance metric for that cell. The comparisoncan explicitly include an allowance for the accuracy of the zonelocation or implicitly include such an allowance via a larger multipleof the distance metric. In one example, there is a cell with ID 38761for which the inter-cell median distance is 1540 m. If the validationmultiplier is set to 2.5, that is, in this network, a cell is notexpected to be hearable at a distance from the cell of 2.5 times theinter-cell distance which in this example is 3850 m. If a cell isreported from in or near a zone that is 10561 m away from the cell, thisdistance exceeds the maximum expected range for the cell and thus thecell is flagged as potentially having incorrect coordinates.

Signal Detection

With regard to the signal detection likelihood metric, the computationis constrained to be evaluated at the known location. An allowance madefor any uncertainty in the location of the zone can be made byincreasing the standard deviation of the signal level. Using a signalpropagation model an appropriate allowance can be computed. If forexample the zone location has an uncertainty of 500 m 2DRMS, the T1P1.5propagation model in a suburban environment at a range of 3000 m fromthe base station indicates that an appropriate allowance would be toincrease the signal strength by 2 dB.

Reusing the example in Table 10, Table 11 and Table 12, the use of thezone location can be illustrated. The nominal location of the zone iscoordinates (455411, 6651528). There is now no need to minimize the costfunction as there is a reliable estimate of the location of the mobile.At the estimated location the estimated signal strengths are −87.8 dBm,−91.6 dBm, and −119.1 dBm for cells A, B, and C respectively.

In the scenario where cells A and B are heard contemporaneously and withthe solution constrained to the nominal location of the zone the cost is0.54. This is well below the 99.9% threshold and the test results do notindicate any detectable problem with cell coordinates.

In the scenario where A and C are heard contemporaneously and with thesolution constrained to the nominal location of the zone the cost is82.5 which is significantly higher than the 99.9% chi-squared thresholdas chosen in the previous example. Thus the measurements indicate thatthere is a potential problem with the coordinates of one or both basestations. Had cell C had the correct coordinates, the cost would havebeen 0.2 and well below the threshold.

Signal Hearability

The nominal zone location can also be combined with a signal propagationmodel to determine if a cell has incorrect errors. For a cell to bedetectable at a given location, the received signal strength, includingany receiver and processing gains, needs to be above the receiver noisefloor and it must be sufficiently strong to be detectable above theinterference. At the nominated location the signal strength can beestimated based on a radio propagation model. Optionally the model caninclude the effects of co-channel interference. Optionally the model cantake into account the effect of adjacent channel interference. Theestimated signal strength is compared to the receiver sensitivity. Ifthe signal is weaker than this value, then the cell potentially hasincorrect coordinates. If the signal is sufficiently strong, it is thencompared to the combined estimated effects of co-channel and adjacentchannel interference. If the signal is not sufficiently strong relativeto the interference, then the cell coordinates may be in error.

In the above example for a GSM network, the estimated signal strength atthe nominated location is −119.1 dBm. The receiver sensitivity for a GSMmobile is approximately −104 dBm. Thus the estimated signal strength is15.1 dBm, which is too weak to be detected and thus the cell may haveincorrect coordinates. If the signal were above the receiversensitivity, then the interference, if being estimated, could then beevaluated. In GSM the signal needs to be 9 dB stronger than the nettinterference to be detected.

Incidental Detection

Measurements made of cell signals are commonly reported only withpartial cell identifiers. For example in GSM, neighbour cells areusually identified only by a BSIC and ARFCN. Serving cells areidentified via their Cell ID. The actual cell associated with eachpartially identified measurement is determined by searching for a matchto the partial identity within the vicinity of the serving cell that hasbeen fully identified via a cell ID. If the neighbour cell has incorrectcoordinates, then the search to find the cell may fail or result in themeasurement being associated with the wrong cell. As such, the failureto find a cell to match reported cell identifiers can be an indicationof a cell with coordinate errors. Similarly, if a serving cell hasincorrect coordinates, one or more neighbour cells may not be able to befully identified based on the BSIC and ARFCN because of the coordinateerror. Thus a coordinate error may manifest via the detection of anunknown cell which will be resolved via a correction to a cell'scoordinates once the root cause is identified.

Efficient Collection of Network Measurements

In one aspect of the invention, the system can optionally leverage thespare capacity in existing messages and/or use already establishedcommunication sessions to report information about the radio network foruse in one or more of the methods described above. In manycommunications networks the protocols available for transmitting thezone status updates or location data are fixed in size, for example SMSin GSM. The status update and location messages do not necessarily useall of the available space. There are also session based communicationsprotocols wherein there is a network bandwidth cost associated withsetting up the session, for example USSD in GSM. Having set up a sessionto send a message, the marginal cost of sending extra data is low. Anadvantage of the present invention is to leverage the available space orsession to send information about the observed radio network at no extracost in terms of network capacity. In systems where the message lengthis variable, the extra information required to support methods accordingto aspects of the present invention can still be appended to status andlocation update messages for a small marginal cost. The network capacitycost of setting up a connection is often such that sending a smallamount of extra data will not significantly impact the system. Thisinformation can be used to support the detection of inconsistenciesbetween the network database and the actual configuration. Theinformation sent can include the identity if serving cell being used bythe mobile, and for each cell heard by the mobile: full (e.g. CID+LAC)and partial cell identifiers (e.g. BSIC, PSC), channel/frequency, signalstrength, and/or variation in signal strength. The data can be the rawmeasurements or filtered (e.g. averaged).

In the process of operating a location or zone based service, asubscriber's handset or mobile terminal periodically exchanges messageswith a network based server. For example, in a home zone serviceoperating as described previously, typically each time the subscribermoves either into or out of the zone, a message is sent notifying theserver. A further advantage of combining data into this message is thespatial coverage that such a spatial trigger provides. The network datagathered will derive from a cross the network coverage area.

For a location service there will be a message sent to the servercontaining either the data in support of a location request or thecoordinate estimate generated in the mobile.

As an example of the spare capacity available in a fixed size messageformat, consider a zone-based location system in which the mobilenotifies the status (in/out) of its zones to a server using SMS. In GSMthe SMS payload is a fixed size of 140 octets. As illustrated in Table23 below, 1 octet is reserved to indicate the type of message, one octetenables up to 8 zone statuses (IN or OUT) to be reported leaving 138octets available for reporting observations of the radio network.

TABLE 23 Msg Zone Type State Radio network observation data 1 octet 1octet 138 octets

Throughout the specification and the claims that follow, unless thecontext requires otherwise, the words “comprise” and “include” andvariations such as “comprising” and “including” will be understood toimply the inclusion of a stated integer or group of integers, but notthe exclusion of any other integer or group of integers.

The reference to any prior art in this specification is not, and shouldnot be taken as, an acknowledgement of any form of suggestion that suchprior art forms part of the common general knowledge

1. A method for detecting an inconsistency between a radiocommunications network and a network database, the method comprising:receiving from a mobile radio terminal in the radio communicationsnetwork, at least one measurement of at least one parameter from themobile radio communications network; comparing the at least onemeasurement with corresponding data in the network database; anddetermining that the at least one measurement is inconsistent if the atleast one measurement is different to the corresponding data in thenetwork database.
 2. A method as claimed in claim 1 further comprisingthe step of calculating a metric associated with the at least onemeasurement using data from the network database, and comparing thecalculated metric with a threshold.
 3. A method as claimed in claim 2further comprising determining that the at least one measurement isdifferent to the corresponding data in the network database if thecalculated metric exceeds the threshold.
 4. A method as claimed in claim1 further comprising making a hypothesis that a parameter of the mobileradio communications network is not present in the mobile radiocommunications network even though data in the network databaseindicates that the parameter is present.
 5. A method as claimed in claim4 further comprising, if the at least one measurement does notcontradict the hypothesis, considering data that supports thehypothesis.
 6. A method as claimed in claim 4 further comprisingconsidering data that supports the hypothesis.
 7. A method as claimed inclaim 6 wherein the step of considering data that supports thehypothesis comprises determining whether the mobile radio terminal is ina given zone.
 8. A method as claimed in claim 7 wherein, if the mobileradio terminal is determined to be in the given zone, the method furthercomprising comparing the at least one measurement with data in thenetwork database corresponding to one or more expected measurements thatwould be expected to be obtained by the mobile radio terminal in thegiven zone.
 9. A method as claimed in claim 8 further comprisingdetermining that there is an inconsistency between the radiocommunications network and the network database if the step of comparingthe at least one measurement with data in the network databasecorresponding to the one or more expected measurements indicates adifference.
 10. A method as claimed in claim 9 further comprisingaccumulating a plurality of measurements over time and determining thatthere is an inconsistency between the radio communications network andthe network database if the difference between the accumulatedmeasurements and the one or more expected measurements exceeds apredetermined threshold.
 11. A method as claimed in claim 10 wherein thehypothesis is that the radio communications network contains anon-operational cell.
 12. A method as claimed in claim 1 wherein the atleast one measurement is received from the mobile radio terminal usingspare capacity in an already established communications session.
 13. Amethod as claimed in claim 12 wherein a plurality of measurements arereceived from a plurality of mobile radio terminals within the radiocommunications network.
 14. A network processor in a radiocommunications network having at least one radio parameter, at least onemobile radio terminal, and a network database, the network databasestoring data corresponding to the at least one radio parameter, thenetwork processor comprising: a receiver for receiving from the mobileradio terminal in the radio communications network, at least onemeasurement of the at least one parameter; a comparator for comparingthe at least one measurement with the corresponding data in the networkdatabase; and a means for determining that the at least one measurementis inconsistent if the at least one measurement is different to thecorresponding data in the network database.
 15. A radio communicationsnetwork comprising a network processor as claimed in claim
 14. 16. Amethod for detecting a non-operational cell in a radio communicationsnetwork, the method comprising: receiving at least one measurement,including data relating to at least one cell, from a mobile radioterminal in the radio communications network; determining whether themobile radio terminal is in a given zone; determining whether the atleast one cell is reported; updating evidence that the at least one cellis not operating; determining whether the updated evidence exceeds apredetermined threshold; and determining that the at least one cell isnot operational if the updated evidence exceeds the predeterminedthreshold.
 17. A method as claimed in claim 16 further comprising foreach cell reported in the at least one measurement, resetting evidenceagainst the at least one cell not operating.
 18. A method as claimed inclaim 16 wherein the step of resetting evidence against the at least onecell not operating comprises setting an accumulated unreported cell costto zero and setting a cell count to zero.
 19. A method as claimed inclaim 17 wherein for each cell unreported in the at least onemeasurement, the step of updating evidence that the at least one cell isnot operating comprises computing an unreported cell cost for the atleast one cell and adding the computed unreported cell cost to theaccumulated unreported cell cost and incrementing the cell count.
 20. Amethod as claimed in claim 18 wherein the step of determining whetherthe updated evidence exceeds the predetermined threshold comprisesdetermining whether the accumulated unreported cell cost is greater thanthe predetermined threshold.
 21. A method as claimed in claim 19 whereinthe at least one cell is determined to be potentially non-operational ifthe accumulated unreported cell cost is greater than the predeterminedthreshold.
 22. A network processor for use in a radio communicationsnetwork having at least one cell in a zone and at least one mobile radioterminal in the radio communications network, the network processorcomprising: a receiver for receiving at least one measurement, includingdata relating to at least one cell, from a mobile radio terminal in theradio communications network; a means for determining whether the mobileradio terminal is in a given zone; determining whether the at least onecell is reported; a means for updating evidence that the at least onecell is not operating; a means for determining whether the updatedevidence exceeds a predetermined threshold; and a means for determiningthat the at least one cell is not operational if the updated evidenceexceeds the predetermined threshold.
 23. A radio communications networkcomprising a network processor as claimed in claim 22.